Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "66" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 45 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 43 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459860 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.366695 | 1.024235 | 1.215642 | 0.197529 | -0.150214 | 0.123361 | -0.217309 | 0.925649 | 0.7190 | 0.6879 | 0.4140 | nan | nan |
| 2459859 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.297964 | 0.580560 | 0.703762 | 0.762632 | -0.474861 | 1.225816 | -0.302858 | 1.541903 | 0.7258 | 0.6939 | 0.4054 | nan | nan |
| 2459858 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 6.335546 | 5.576407 | 1.102822 | 0.856391 | 0.191036 | 1.960810 | -0.161652 | 1.312138 | 0.6796 | 0.6507 | 0.3983 | 2.781927 | 2.413641 |
| 2459857 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 13.810116 | 11.370614 | 4.316254 | 3.410482 | 10.365606 | 4.408113 | 7.641017 | 12.965069 | 0.0222 | 0.0223 | 0.0003 | nan | nan |
| 2459856 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 3.43% | 0.57% | 0.680562 | 1.667983 | 0.327800 | 0.118056 | 0.173344 | -0.184740 | -0.684607 | 2.223085 | 0.7268 | 0.7144 | 0.4039 | 1.633942 | 1.488764 |
| 2459855 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 4.58% | 0.00% | 0.426879 | 1.726831 | 0.764003 | -0.159714 | 1.883220 | -0.260250 | -0.474965 | 0.581087 | 0.7096 | 0.7312 | 0.4343 | 1.671364 | 1.454142 |
| 2459854 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 8.93% | 0.00% | 0.229829 | 1.756626 | 0.253109 | -0.388482 | 1.260359 | -0.065324 | -0.291711 | 1.339571 | 0.7228 | 0.7480 | 0.4430 | 1.656487 | 1.419558 |
| 2459853 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.411780 | 1.319160 | 0.339961 | -0.541229 | 0.584651 | 0.046763 | -0.415615 | 2.045726 | 0.7497 | 0.7061 | 0.4239 | 1.757247 | 1.500345 |
| 2459852 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 8.65% | 0.54% | 1.425655 | 0.189018 | 1.297147 | -0.635562 | 1.731441 | -0.028741 | 1.068658 | -0.849472 | 0.8293 | 0.8400 | 0.2455 | 2.911738 | 3.116370 |
| 2459851 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 11.76% | 0.00% | 0.760308 | 1.708326 | 0.626757 | -0.263845 | -0.268790 | 0.821808 | -0.666738 | 0.906455 | 0.7668 | 0.7581 | 0.3436 | 1.475416 | 1.428654 |
| 2459850 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 18.02% | 0.00% | 0.423727 | 1.903279 | 0.310877 | -0.340983 | 0.384609 | -0.867530 | -0.499859 | 1.416286 | 0.7485 | 0.7687 | 0.3628 | 1.662230 | 1.396480 |
| 2459849 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 16.67% | 0.00% | 0.719815 | 2.659970 | -0.409305 | -0.164041 | 1.600886 | 0.745678 | -0.364080 | 2.372809 | 0.7476 | 0.7604 | 0.3667 | 1.508056 | 1.406733 |
| 2459848 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 30.15% | 0.00% | 1.123736 | 2.206189 | -0.985534 | 1.709172 | 0.178443 | -0.196639 | -0.536614 | 1.721658 | 0.7253 | 0.7593 | 0.3923 | 1.354773 | 1.291827 |
| 2459847 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.170577 | 2.451512 | -0.250378 | 1.434724 | 4.455473 | -0.719775 | -0.637685 | 0.088817 | 0.7376 | 0.7029 | 0.4290 | 3.514081 | 3.299473 |
| 2459846 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 33.33% | 0.00% | 1.030988 | 1.562907 | -0.026830 | 0.903306 | 1.263748 | 0.288532 | 0.011499 | 0.996411 | 0.8460 | 0.6957 | 0.4728 | 1.569642 | 1.407474 |
| 2459845 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 16.02% | 83.98% | 1.931345 | 3.103888 | -0.492395 | 1.811661 | 2.664914 | 0.451592 | -0.668933 | 0.331688 | 0.7357 | 0.7556 | 0.3781 | 4.539539 | 6.671874 |
| 2459844 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 32.436641 | 23.227586 | 22.521189 | 21.918292 | 8.401513 | 5.221339 | 9.165380 | 15.188162 | 0.0221 | 0.0216 | 0.0004 | nan | nan |
| 2459843 | digital_ok | 0.00% | 0.66% | 0.66% | 0.00% | 16.30% | 0.00% | 0.665943 | 3.121541 | 0.526941 | 0.843845 | 1.445980 | 1.433840 | -0.438823 | 2.541452 | 0.7490 | 0.7576 | 0.3820 | 1.832510 | 1.674130 |
| 2459840 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 65.646825 | 47.254281 | 14.995430 | 13.405704 | 6.342115 | 6.390632 | 14.500856 | 19.054897 | 0.0196 | 0.0198 | 0.0004 | nan | nan |
| 2459839 | digital_ok | 100.00% | - | - | - | - | - | 16.764180 | 11.636731 | 35.746582 | 32.440256 | 2.178643 | 2.818910 | 19.400324 | 27.104094 | nan | nan | nan | nan | nan |
| 2459838 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 239.584135 | 239.688580 | inf | inf | 11664.605066 | 11706.093746 | 7714.235927 | 7791.778639 | nan | nan | nan | 0.000000 | 0.000000 |
| 2459836 | digital_ok | - | 100.00% | 100.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.0316 | 0.0312 | 0.0020 | nan | nan |
| 2459835 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 0.529995 | -0.495363 | 1.902543 | -0.877460 | 23.767892 | 22.906235 | 97.885564 | 87.500123 | 0.0314 | 0.0308 | 0.0020 | nan | nan |
| 2459833 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 6.500193 | 4.264227 | 1.238807 | 0.574219 | 52.208257 | 45.172192 | 127.426395 | 125.682570 | 0.0242 | 0.0243 | 0.0006 | nan | nan |
| 2459832 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 1.881605 | 1.119455 | 0.093297 | 0.313249 | 1.736729 | 0.427030 | -0.450379 | 0.372458 | 0.8143 | 0.5599 | 0.5563 | 1.784596 | 1.731096 |
| 2459831 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 17.203616 | 11.955180 | 38.812345 | 35.366557 | 3.272949 | 3.526635 | 14.361110 | 18.851245 | 0.0203 | 0.0205 | 0.0004 | nan | nan |
| 2459830 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 1.755648 | 1.246778 | -0.668566 | 0.509119 | 0.950649 | 2.194637 | -0.261617 | 2.055243 | 0.8150 | 0.5797 | 0.5287 | 1.782928 | 1.487410 |
| 2459829 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 3.043141 | 3.293751 | -0.546713 | 1.136572 | 0.869699 | 2.313590 | 0.972199 | 5.673217 | 0.7705 | 0.6910 | 0.3871 | 13.949290 | 11.799677 |
| 2459828 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.028892 | 1.388962 | -0.732024 | 0.965504 | 0.087055 | 2.203386 | 0.276269 | 7.849303 | 0.8122 | 0.5840 | 0.5176 | 5.702732 | 5.630875 |
| 2459827 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 2.171244 | 2.380105 | -0.683454 | 1.267503 | -0.115912 | 2.089667 | -0.708239 | 0.844789 | 0.7718 | 0.6958 | 0.3901 | 1.576940 | 1.300739 |
| 2459826 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 1.525870 | 0.931245 | -0.612794 | 0.532906 | 0.546839 | 1.499041 | -0.466711 | 2.233071 | 0.8065 | 0.5918 | 0.5011 | 1.513045 | 1.115815 |
| 2459825 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 1.103509 | 0.669220 | -0.206301 | 0.636361 | 0.100534 | 1.863076 | -0.802195 | -0.318218 | 0.8095 | 0.6103 | 0.4958 | 2.111676 | 1.613448 |
| 2459824 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 1.163962 | 1.503208 | 0.676358 | 1.062148 | 0.844831 | -0.135968 | -0.347990 | 1.101498 | 0.7387 | 0.7572 | 0.3439 | 2.188693 | 2.265444 |
| 2459823 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.569572 | -0.191054 | -0.559088 | 0.204140 | 1.210198 | 0.117794 | 0.691408 | 5.543695 | 0.7797 | 0.6758 | 0.4365 | 10.286593 | 12.268885 |
| 2459822 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.996567 | 0.552257 | -0.837092 | 0.703444 | -0.120824 | 2.659143 | 0.157757 | 0.682587 | 0.8070 | 0.6287 | 0.4844 | 1.871528 | 1.522775 |
| 2459821 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.825096 | -0.129023 | -0.859593 | 0.413597 | 0.007015 | 1.447572 | -0.579707 | 0.256273 | 0.8030 | 0.6437 | 0.4935 | 1.881384 | 1.543367 |
| 2459820 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.158825 | 2.024507 | -0.783751 | 1.121316 | 1.029734 | 7.149627 | -0.073956 | 3.383468 | 0.7801 | 0.7118 | 0.4039 | 4.674860 | 5.020066 |
| 2459817 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.067456 | -0.348731 | -0.878149 | 0.469787 | 0.071250 | 1.850125 | -0.362911 | 0.516614 | 0.8128 | 0.6783 | 0.4862 | 1.884697 | 1.695681 |
| 2459816 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459815 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.390019 | -0.242433 | -0.403591 | 0.335547 | 1.696799 | 1.313180 | -0.334758 | 2.702845 | 0.8090 | 0.6936 | 0.4921 | 1.664877 | 1.424915 |
| 2459814 | digital_ok | 0.00% | - | - | - | - | - | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459813 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.711806 | 2.955822 | -0.895085 | 0.333988 | 1.398184 | 6.037809 | 0.783219 | 6.873374 | 0.8043 | 0.7587 | 0.3778 | 0.000000 | 0.000000 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | ee Power | 1.215642 | 0.366695 | 1.024235 | 1.215642 | 0.197529 | -0.150214 | 0.123361 | -0.217309 | 0.925649 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Temporal Discontinuties | 1.541903 | 0.297964 | 0.580560 | 0.703762 | 0.762632 | -0.474861 | 1.225816 | -0.302858 | 1.541903 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | ee Shape | 6.335546 | 5.576407 | 6.335546 | 0.856391 | 1.102822 | 1.960810 | 0.191036 | 1.312138 | -0.161652 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | ee Shape | 13.810116 | 11.370614 | 13.810116 | 3.410482 | 4.316254 | 4.408113 | 10.365606 | 12.965069 | 7.641017 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Temporal Discontinuties | 2.223085 | 0.680562 | 1.667983 | 0.327800 | 0.118056 | 0.173344 | -0.184740 | -0.684607 | 2.223085 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | ee Temporal Variability | 1.883220 | 1.726831 | 0.426879 | -0.159714 | 0.764003 | -0.260250 | 1.883220 | 0.581087 | -0.474965 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Shape | 1.756626 | 1.756626 | 0.229829 | -0.388482 | 0.253109 | -0.065324 | 1.260359 | 1.339571 | -0.291711 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Temporal Discontinuties | 2.045726 | 1.319160 | 0.411780 | -0.541229 | 0.339961 | 0.046763 | 0.584651 | 2.045726 | -0.415615 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | ee Temporal Variability | 1.731441 | 1.425655 | 0.189018 | 1.297147 | -0.635562 | 1.731441 | -0.028741 | 1.068658 | -0.849472 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Shape | 1.708326 | 0.760308 | 1.708326 | 0.626757 | -0.263845 | -0.268790 | 0.821808 | -0.666738 | 0.906455 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Shape | 1.903279 | 0.423727 | 1.903279 | 0.310877 | -0.340983 | 0.384609 | -0.867530 | -0.499859 | 1.416286 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Shape | 2.659970 | 0.719815 | 2.659970 | -0.409305 | -0.164041 | 1.600886 | 0.745678 | -0.364080 | 2.372809 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Shape | 2.206189 | 2.206189 | 1.123736 | 1.709172 | -0.985534 | -0.196639 | 0.178443 | 1.721658 | -0.536614 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | ee Temporal Variability | 4.455473 | 2.451512 | 1.170577 | 1.434724 | -0.250378 | -0.719775 | 4.455473 | 0.088817 | -0.637685 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Shape | 1.562907 | 1.030988 | 1.562907 | -0.026830 | 0.903306 | 1.263748 | 0.288532 | 0.011499 | 0.996411 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Shape | 3.103888 | 3.103888 | 1.931345 | 1.811661 | -0.492395 | 0.451592 | 2.664914 | 0.331688 | -0.668933 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | ee Shape | 32.436641 | 32.436641 | 23.227586 | 22.521189 | 21.918292 | 8.401513 | 5.221339 | 9.165380 | 15.188162 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Shape | 3.121541 | 3.121541 | 0.665943 | 0.843845 | 0.526941 | 1.433840 | 1.445980 | 2.541452 | -0.438823 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | ee Shape | 65.646825 | 65.646825 | 47.254281 | 14.995430 | 13.405704 | 6.342115 | 6.390632 | 14.500856 | 19.054897 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | ee Power | 35.746582 | 11.636731 | 16.764180 | 32.440256 | 35.746582 | 2.818910 | 2.178643 | 27.104094 | 19.400324 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Power | inf | 239.688580 | 239.584135 | inf | inf | 11706.093746 | 11664.605066 | 7791.778639 | 7714.235927 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | ee Temporal Discontinuties | 97.885564 | -0.495363 | 0.529995 | -0.877460 | 1.902543 | 22.906235 | 23.767892 | 87.500123 | 97.885564 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | ee Temporal Discontinuties | 127.426395 | 4.264227 | 6.500193 | 0.574219 | 1.238807 | 45.172192 | 52.208257 | 125.682570 | 127.426395 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | ee Shape | 1.881605 | 1.881605 | 1.119455 | 0.093297 | 0.313249 | 1.736729 | 0.427030 | -0.450379 | 0.372458 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | ee Power | 38.812345 | 17.203616 | 11.955180 | 38.812345 | 35.366557 | 3.272949 | 3.526635 | 14.361110 | 18.851245 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Temporal Variability | 2.194637 | 1.755648 | 1.246778 | -0.668566 | 0.509119 | 0.950649 | 2.194637 | -0.261617 | 2.055243 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Temporal Discontinuties | 5.673217 | 3.293751 | 3.043141 | 1.136572 | -0.546713 | 2.313590 | 0.869699 | 5.673217 | 0.972199 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Temporal Discontinuties | 7.849303 | 1.388962 | 2.028892 | 0.965504 | -0.732024 | 2.203386 | 0.087055 | 7.849303 | 0.276269 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Shape | 2.380105 | 2.171244 | 2.380105 | -0.683454 | 1.267503 | -0.115912 | 2.089667 | -0.708239 | 0.844789 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Temporal Discontinuties | 2.233071 | 0.931245 | 1.525870 | 0.532906 | -0.612794 | 1.499041 | 0.546839 | 2.233071 | -0.466711 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Temporal Variability | 1.863076 | 0.669220 | 1.103509 | 0.636361 | -0.206301 | 1.863076 | 0.100534 | -0.318218 | -0.802195 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Shape | 1.503208 | 1.163962 | 1.503208 | 0.676358 | 1.062148 | 0.844831 | -0.135968 | -0.347990 | 1.101498 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Temporal Discontinuties | 5.543695 | -0.191054 | 0.569572 | 0.204140 | -0.559088 | 0.117794 | 1.210198 | 5.543695 | 0.691408 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Temporal Variability | 2.659143 | 0.996567 | 0.552257 | -0.837092 | 0.703444 | -0.120824 | 2.659143 | 0.157757 | 0.682587 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Temporal Variability | 1.447572 | -0.129023 | 0.825096 | 0.413597 | -0.859593 | 1.447572 | 0.007015 | 0.256273 | -0.579707 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Temporal Variability | 7.149627 | 2.158825 | 2.024507 | -0.783751 | 1.121316 | 1.029734 | 7.149627 | -0.073956 | 3.383468 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Temporal Variability | 1.850125 | 0.067456 | -0.348731 | -0.878149 | 0.469787 | 0.071250 | 1.850125 | -0.362911 | 0.516614 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Temporal Discontinuties | 2.702845 | -0.242433 | 0.390019 | 0.335547 | -0.403591 | 1.313180 | 1.696799 | 2.702845 | -0.334758 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Temporal Discontinuties | 6.873374 | 2.955822 | 2.711806 | 0.333988 | -0.895085 | 6.037809 | 1.398184 | 6.873374 | 0.783219 |